I’ve recently had my writing analytics work published at the 21st international conference on artificial intelligence in education (AIED 2020) where the theme was “Augmented Intelligence to Empower Education”. It is a short paper describing a text analysis and visualisation method to study revisions. It introduced ‘Automated Revision Graphs’ to study revisions in short texts at a sentence level by visualising text as graph, with open source code.
I did a short introductory video for the conference, which can be viewed below:
I also had another paper I co-authored on multi-modal learning analytics lead by Roberto Martinez, which received the best paper award in the conference. The main contribution of the paper is a set of conceptual mappings from x-y positional data (captured from sensors) to meaningful measurable constructs in physical classroom movements, grounded in the theory of Spatial Pedagogy. Great effort by the team!
Swales pattern for research articles: Introduction, Methods, Results, Discussion (IMRD) and Creating a Research Space (CARS) model.
Studying the rhetorical structure of tests is found to be useful to aid reading and writing (Mover tool notes here).
To automatically analyze move structures (Background, Purpose, Method, Result, and Conclusion) from research article abstracts.
To develop an online learning system CARE (Concordancer for Academic wRiting in English) using move structures to help novice writers.
TANGO Concordancer used for extracting collocations with chunking and clause information – Sample Verb-Noun collocation structures in corpus: VP+NP, VP+PP+NP, and VP+NP+PP (Ref: Jian, J. Y., Chang, Y. C., & Chang, J. S. (2004, July). TANGO: Bilingual collocational concordancer. In Proceedings of the ACL 2004 on Interactive poster and demonstration sessions (p. 19). Association for Computational Linguistics.)
Data: Corpus of 20,306 abstracts (95,960 sentences) from Citeseer. Manual tagging of moves in 106 abstracts containing 709 sentences. 72,708 collocation types extracted and manually tagged 317 collocations with moves.
Hidden Markov Model (HMM) trained using 115 abstracts containing 684 sentences.
Different parameters evaluated for the HMM model: “the frequency of collocation types, the number of sentences with collocation in each abstract, move sequence score and collocation score”
Precision of 80.54% achieved when 627 sentences were qualified with following parameters: weight of transitional probability function 0.7 , frequency threshold for a collocation to be applicable – 18 (crucial to exclude unreliable collocation).
CARE system interface created for querying and looking up sentences for a specific move.
System is expected to help non native speakers write abstracts for research articles.